The patient \(P_{i}\)  logins the account and sends his/her vital data captured by IoMT to the medical professional \(MP_{i}\)  by encrypting the message with the secret shared key \(K_{s}\)  established during the mutual authentication process. Gupta et al. [1]} proposed the anomaly detection model for the RPM ecosystem by analyzing the patient's behavior using both smart home and smart health devices. A traditional RPM ecosystem includes only IoMT, which makes it challenging to identify misbehaving sensor nodes. For instance, a smart blood pressure machine reports hypertension of 150, which is identified as an anomaly. However, it could be a false alarm due to various reasons, including faulty sensors, data poisoning, and intruders. In order to accurately identify anomalies, we develop the co-relations among smart home and smart health sensors.
